CHD risk stratification evaluations for subjects with high levels of large HDL-P

ABSTRACT

Embodiments of the invention are directed to methods, systems and computer programs that provide improved risk stratification for people having elevated large HDL-P using at least one defined HDL risk interaction parameter.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalApplication Ser. No. 61/639,508, filed Apr. 27, 2012, the contents ofwhich are hereby incorporated by reference as if recited in full herein.

FIELD OF THE INVENTION

The present invention relates generally to analysis of lipoproteinconstituents in blood plasma and serum.

BACKGROUND OF THE INVENTION

NMR spectroscopy has been used to concurrently measure very low densitylipoprotein (VLDL), low density lipoproteins (LDL) and high densitylipoproteins (HDL) as VLDL, LDL and HDL particle subclasses from invitro blood plasma or serum samples. See, FIG. 1 and U.S. Pat. Nos.4,933,844 and 6,617,167, the contents of which are hereby incorporatedby reference as if recited in full herein. Generally stated, to evaluatethe lipoproteins in a blood plasma and/or serum sample, the amplitudesof a plurality of NMR spectroscopy derived signals within a chemicalshift region of the NMR spectrum are derived by deconvolution of thecomposite methyl signal envelope or spectrum to yield subclassconcentrations.

The subclasses are represented by many (typically over 60) discretecontributing subclass signals associated with NMR frequency andlipoprotein diameter as shown in FIG. 2. As shown in FIG. 3, the NMRevaluations can interrogate the NMR signals to produce concentrations ofdifferent subpopulations shown as seventy-three (73) discretesubpopulations, 27 for VLDL, 20 for LDL and 26 for HDL. Thesesub-populations can be further characterized as associated with aparticular size range within the VLDL, LDL or HDL subclasses.

Conventionally, a patient's overall risk of coronary heart disease (CHD)and/or coronary artery disease (CAD) has been assessed based onmeasurements of cholesterol content of a patient's LDL and HDL particles(LDL-C, HDL-C) rather than the numbers of these particles. These tworisk factors are used to assess a patient's risk, and treatmentdecisions may be made to reduce the “bad” cholesterol (LDL-C) orincrease the “good” cholesterol (HDL-C).

In the past, the LipoProfile® “advanced” lipoprotein test panels fromLipoScience, Inc. have typically included a total high densitylipoprotein particle (HDL-P) measurement (e.g., HDL-P number) and atotal low density lipoprotein particle (LDL-P) measurement (e.g., LDL-Pnumber). The particle numbers represent the concentration in units suchas nmol/L (for LDL-P) or μmol/L (for HDL-P). A total HDL-P number, thesum of the concentration values of each of the HDL-P subclasses, canprovide CHD risk assessment information that may be more accurate thanor complement HDL-C.

It is believed that LDL-P is a better indicator of risk of CHD relativeto LDL-C as well as for therapy decisions. However, there are still openquestions about the different functions of HDL and how to best evaluateCHD risk associated with a patient's HDL. See, e.g., Kher at el.,Cholesterol Efflux Capacity, High-Density Lipoprotein Function, andAthersclerosis, N Engl. J. Med. 364: 127-135 (Jan. 13, 2011); Navab etal., HDL and cardiovascular disease: atherogenic and atheroprotectivemechanisms, Nat. Rev. Cardiol., 8, 222-232 (2011); and Alan Fogelman,When good cholesterol goes bad, Nat. Med., Vol. 10, No. 9, pp. 902-903(September 2004), the contents of which are hereby incorporated byreference as if recited in full herein. The mechanisms by which HDL canbe protective or non-protective as associated with a person's risk ofdeveloping atherosclerosis or heart disease are complex andmultifactorial. See, Farmer et al., Evolving Concepts of the Role ofHigh-Density Lipoprotein in Protection from Athersclerosis, CurrAtheroscler Rep (2011) 13:107-114, the contents of which are herebyincorporated by reference as if recited in full herein.

Van der Steeg et al. have carried out studies showing that higher HDL-Clevels when observed with a preponderance of large HDL particles are notinversely related to the risk of CAD. Indeed, higher HDL-C proved to bea major cardiac event risk factor when adjusted for age, gender,smoking, apoA-1 and apoB. Van der Steeg et. al. concludes that whenapoA-1 and apoB are kept constant, HDL-C and HDL particle size mayconfer risk at very high values. See, Van der Steeg et. al.,High-Density Lipoprotein Cholesterol, High Density Lipoprotein ParticleSize, and Apolipoprotein A-1: Significance for Cardiovascular Risk,JACC, Vol. 51, No. 6, 2008 (634-642), the contents of which are herebyincorporated by reference as if recited in full herein.

There remains an unmet clinical need for tests that can identify thoseindividuals that have high levels of HDL, e.g., large HDL-P and that maybe at increased risk of a cardiac event.

SUMMARY

Embodiments of the invention are directed at methods, systems, andcomputer program products for screening, assessing and/or evaluatingwhether a person having (i) a high level of HDL-C and along withelevated concentrations of very large HDL-P or (ii) elevatedconcentrations of very large HDL-P is at risk of having or developing atleast one of CHD, stroke or atherosclerosis.

Embodiments of the invention provide an NMR screening test to identifywhether a person having a high level of very large HDL-P may be atincreased risk of having or developing at least one of CVD, CHD, strokeor atherosclerosis.

Embodiments of the invention carry out a screening, assessment and/orevaluation only on patients having elevated levels of very large HDLparticle subclasses (H21-H26) at ≧80% of a population norm.

Embodiments of the invention can be applied to patient evaluations onlywhen HDL-C is above 60 mg/dL, then when they also have elevated levelsat ≧80% of a population norm of very large HDL particle subclasses(H21-H26).

The screening, assessment and/or evaluation test can employ at least onedefined interaction HDL risk parameter that includes a sum, product orratio of two or more of the following: HP_(VL), HP_(VS) and HP_(ML).

The screening, assessment and/or evaluation test can include at leastone of P1 (HP_(VS)×HP_(ML)) or R1 (HP_(VL)/HP_(ML)) as an HDL riskparameter.

The screening, assessment and/or evaluation test can include both P1 andR1.

The screening, assessment and/or evaluation test can include P1/R1 andpatients having a value in the first quartile or the first and secondquartile are identified as at increased risk.

Certain embodiments of the present invention are directed at providingmethods, systems, and computer program products that discriminatebetween subclasses of HDL particles of a discrete size range taken froma blood plasma or serum sample to facilitate patient risk stratificationfor patients presenting with high levels of very large HDL-P subclasses.

Some embodiments are directed to methods of determining whether asubject with elevated concentrations of very large high densitylipoprotein (HDL) particles (HDL-P) is at increased risk for a cardiacevent and/or CHD. The methods include programmatically calculating atleast one HDL interaction risk parameter associated with HDL content ofa blood plasma or serum sample of the subject. The at least one HDLinteraction risk parameter includes at least two of HP_(VS), HP_(ML),and HP_(VL), where HP_(VS) is a concentration of very small HDL-Psubclasses, HP_(ML) is a concentration of medium and large HDL-Psubclasses, and HP_(VL) is a concentration of very large HDL-Psubclasses.

The at least one HDL interaction risk parameter can include P1 or R1, orP1 and R1, wherein P1 is a product defined by HP_(VS)×HP_(ML), andwherein R1 is a ratio defined by HP_(VL)/HP_(ML).

The at least one HDL interaction risk parameter can include at least oneof the following: (HP_(VS))(HP_(ML))/(HP_(VL)), or ((HP_(VS))(HP_(ML))²)/(HP_(VL)), or (HP_(ML))²/(HP_(VL)), wherein P1 is a productdefined by HP_(VS)×HP_(ML), and wherein R1 is a ratio defined byHP_(VL)/HP_(ML).

The method may include electronically identifying when a subject has anelevated concentration of HP_(VL) relative to a population norm, whereinthe subject has an elevated concentration of HP_(VL) when a respectiveconcentration is ≧80% of a population norm, and wherein the programmaticcalculation is electronically selectively carried out only when thesubject has the elevated concentration of HP_(VL).

The method may include screening subjects that may benefit from an HDLrisk stratification test by (a) first identifying if the subject haselevated high density lipoprotein-cholesterol (HDL-C) that is at leastone of: ≧60 mg/dL, ≧80 mg/dL, or ≧100 mg/dL; then (b) electronicallyidentifying when the subject also has an elevated concentration ofHP_(VL) that is ≧80% of a population norm, and wherein the performingthe programmatic calculation is selectively carried out only if thesubject has the elevated concentration of HP_(VL).

The HP_(VS) can include HDL subpopulations having a size between 7.4 nmand 7.6 nm (average).

The HP_(ML) can include HDL subpopulations having a size between 8.3nm-10.9 nm (average).

The HP_(VL) can include HDL subpopulations having a size between 11 nmto 13.5 nm (average).

The method can include deconvolving an NMR composite signal into 26subpopulations (H1-H26) of different sizes of HDL-P ranging from asmallest HDL-P size associated with H1 to a largest HDL-P sizeassociated with H26, then:

electronically calculating concentrations of H1 and H2 to generate theHP_(VS);

electronically calculating concentrations of H9-H20 to generate theHP_(ML); and

electronically calculating concentrations of H21 and H26 to generate theHP_(VL).

The programmatically calculated at least one HDL interaction riskparameter can include P1/R1.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue in the first or second quartile of a population norm.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue ≦170 μmol²/L².

The method can include generating a report that visually and/ortextually indicates whether a respective subject is at increased risk ofCHD despite having elevated HP_(VL).

The method can include generating a report based on the programmaticcalculation of the at least one HDL interaction risk parameter includesat least one of the following: R1, P1, P1/R1, or P1+R1.

The method can include electronically monitoring whether there is achange in the HDL risk interaction parameter over time to assess achange in CHD risk when HP_(VL), remains above 1.84 μmol/L.

The method can include referring the subject for further medicalevaluation if the programmatic calculation indicates there is alikelihood of increased risk of CHD despite the elevated large HDL-P.

The subject can be human.

The method can include obtaining NMR signal data of an in vitro bloodplasma or serum sample of the subject to determine NMR derivedconcentration measurements. The obtaining and calculating steps can becarried out using at least one processor. The method can includeproviding a report indicating whether a respective subject is at risk ofhaving and/or developing CHD based, in part, on the programmaticcalculation.

Other embodiments are directed to computer program products forstratifying CHD risk for patients with elevated concentrations of verylarge high density lipoprotein (HDL) particles (HDL-P). The computerprogram products can include a non-transitory computer readable storagemedium having computer readable program code embodied in the medium. Thecomputer-readable program code includes: (a) computer readable programcode that obtains concentration measurements of at least twentysubpopulations of HDL-P subclasses in a blood plasma or serum sample;and (b) computer readable program code that calculates at least one HDLinteraction risk parameter associated with HDL content of a blood plasmaor serum sample of the subject. The at least one HDL interaction riskparameter includes at least two of HP_(VS), HP_(ML), and HP_(VL), whereHP_(VS) is a concentration of very small HDL-P subclasses, HP_(ML) is aconcentration of medium and large HDL-P subclasses, and HP_(VL) is aconcentration of very large HDL-P subclasses.

The at least one HDL interaction risk parameter can include P1 or R1, orP1 and R1. P1 is a product defined by HP_(VS)×HP_(ML), and R1 is a ratiodefined by HP_(VL)/HP_(ML).

The at least one HDL interaction risk parameter can include at least oneof the following: (HP_(VS))(HP_(ML))/(HP_(VL)), or((HP_(VS))(HP_(ML))²)/(HP_(VL)), or (HP_(VL))²/(HP_(VL)),

wherein P1 is a product defined by HP_(VS)×HP_(ML), and wherein R1 is aratio defined by HP_(VL)/HP_(ML).

The computer readable program code can include computer readable programcode that identifies when a subject has an elevated concentration ofHP_(VL) relative to a population norm. The subject can have an elevatedconcentration of HP_(VL) when a respective concentration is ≧80% of apopulation norm. The computer readable program code that calculates theat least one HDL interaction risk factor can be configured toselectively be performed only when the subject has the elevatedconcentration of HP_(VL).

The computer program product can include comprising computer readableprogram code that screens subjects that may benefit from an HDL riskstratification test by (a) first identifying if the subject has elevatedhigh density lipoprotein-cholesterol (HDL-C) that is at least one of:≧60 mg/dL, ≧80 mg/dL, or ≧100 mg/dL; then (b) electronically identifyingwhen the subject also has an elevated concentration of HP_(VL) that is≧80% of a population norm, then directing the programmatic calculationif the subject has the elevated concentration of HP_(VL).

The HP_(VS) includes HDL subpopulations having a size between 7.4 nm and7.6 nm (average), wherein the HP_(ML) includes HDL subpopulations havinga size between 8.3 nm-10.9 nm (average), and wherein the HP_(VL)includes HDL subpopulations having a size between 11 nm to 13.5 nm(average).

The computer program product can include computer readable program codeconfigured to deconvolve an NMR composite signal into 26 subpopulations(H1-H26) of different sizes of HDL-P ranging from a smallest HDL-P sizeassociated with H1 to a largest HDL-P size associated with H26, then:

calculate concentrations of H1 and H2 to generate the HP_(VS);

calculate concentrations of H9-H20 to generate the HP_(ML); and

calculate concentrations of H21 and H26 to generate the HP_(VL).

The at least one HDL interaction risk parameter includes P1/R1.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue in the first or second quartile of a population norm.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue ≦170 μmol²/L².

The computer program product can include program code that generates areport that visually and/or textually indicates whether a respectivesubject is at increased risk of CHD despite having elevated HP_(VL).

The computer program product can include computer program code thatgenerates a report including at least one of the following as the atleast one HDL interaction risk parameter: R1, P1, P1/R1, or P1+R1.

The computer program product can include computer readable program codeconfigured to electronically generate a graph showing, provide datarepresenting and/or monitor whether there is a change in the HDL riskinteraction parameter of a respective subject over time to assess achange in CHD risk when HP_(VL) remains above 1.84 μmol/L.

The product can include computer readable program code that obtains NMRsignal data of an in vitro blood plasma or serum sample of the subjectto determine NMR derived concentration measurements and computerreadable program code that provides a report indicating whether arespective subject is at risk of having and/or developing CHD based, inpart, on the at least one HDL interaction risk parameter.

Still other embodiments are directed to systems for analyzing CHD risk.The systems include a circuit comprising at least one processorconfigured to determine whether a subject with elevated concentrationsof very large high density lipoprotein (HDL) particles (HDL-P) is atincreased risk for a cardiac event and/or CHD. The at least oneprocessor is configured to calculate at least one HDL interaction riskparameter associated with HDL content of a blood plasma or serum sampleof the subject, the at least one HDL interaction risk parameter thatincludes at least two of HP_(VS), HP_(ML), and HP_(VL), where HP_(VS) isa concentration of very small HDL-P subclasses, HP_(ML) is aconcentration of medium and large HDL-P subclasses, and HP_(VL) is aconcentration of very large HDL-P subclasses.

The circuit can be onboard or in communication with an NMR spectrometerfor acquiring at least one NMR spectrum of an in vitro blood plasma orserum sample.

The at least one HDL interaction risk parameter can include P1 or R1, orP1 and R1 (or both). P1 is a product defined by HP_(VS)×HP_(ML) and R1is a ratio defined by HP_(VL)/HP_(ML).

The at least one HDL interaction risk parameter can include at least oneof the following: (HP_(VS))(HP_(ML))/(HP_(VL)), or ((HP_(VS))(HP_(ML))²)(HP_(VL)), or (HP_(ML))²/(HP_(VL)); wherein P1 is a product defined byHP_(VS)×HP_(ML), and wherein R1 is a ratio defined by HP_(VL)/HP_(ML).

The at least one processor can be configured to identify when a subjecthas an elevated concentration of HP_(VL) relative to a population norm,wherein the subject has an elevated concentration of HP_(VL) when arespective concentration is ≧80% of a population norm, and wherein theat least one processor is configured to provide a CHD risk assessmentusing the at least one HDL interaction risk parameter only when thesubject has the elevated concentration of HP_(VL).

The at least one processor is configured to screen subjects that maybenefit from an HDL risk stratification test by (a) first identifying ifthe subject has elevated high density lipoprotein-cholesterol (HDL-C)that is at least one of: ≧60 mg/dL, ≧80 mg/dL, or ≧100 mg/dL; then (b)identifying when the subject also has an elevated concentration ofHP_(VL) that is ≧80% of a population norm.

The HP_(VS) can include HDL subpopulations having a size between 7.4 nmand 7.6 nm (average), wherein the HP_(ML) includes HDL subpopulationshaving a size between 8.3 nm-10.9 nm (average), and the HP_(VL) caninclude HDL subpopulations having a size between 11 nm to 13.5 nm(average).

The at least one processor can be configured to deconvolve an NMRcomposite signal into 26 subpopulations (H1-H26) of different sizes ofHDL-P ranging from a smallest HDL-P size associated with H1 to a largestHDL-P size associated with H26, then:

calculate concentrations of H1 and H2 to generate the HP_(VS);

calculate concentrations of H9-H20 to generate the HP_(ML); and

calculate concentrations of H21 and H26 to generate the HP_(VL).

The at least one HDL interaction risk parameter can include P1/R1.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue in the first or second quartile of a population norm.

A respective subject can be identified as at risk for CHD if P1/R1 has avalue ≦170 μmol²/L².

The at least one processor can be configured to generate a report thatvisually and/or textually indicates whether a respective subject is atincreased risk of CHD despite having elevated HP_(VL).

The at least one HDL interaction risk parameter can include at least oneof the following: R1, P1, P1/R1, or P1+R1.

The at least one processor can be configured to monitor whether there isa change in the at least one HDL risk interaction parameter over time toassess a change in CHD risk when HP_(VL) remains above 1.84 μmol/L.

The at least one processor can be configured to obtain NMR signal dataof an in vitro blood plasma or serum sample of the subject to determineNMR derived concentration measurements, then provide a report indicatingwhether a respective subject is at risk of having and/or developing CHDbased, in part, on the calculated at least one HDL interaction riskparameter.

Still other embodiments are directed to patient test reports. Thereports include at least one HDL risk interaction parameter thatindicates whether a patient having elevated concentrations of very largehigh density lipoprotein (HDL) particles (HDL-P) is at increased riskfor a cardiac event and/or CHD. The at least one HDL interaction riskparameter includes at least two of HP_(VS), HP_(ML), and HP_(VL), whereHP_(VS) is a concentration of very small HDL-P subclasses, HP_(ML) is aconcentration of medium and large HDL-P subclasses, and HP_(VL) is aconcentration of very large HDL-P subclasses.

The at least one HDL interaction risk parameter can include P1 or R1, orP1 and R1, wherein P1 is a product defined by HP_(VS)×HP_(ML), R1 is aratio defined by HP_(VL)/HP_(ML).

The at least one HDL interaction risk parameter can include at least oneof the following: (HP_(VS))(HP_(ML))/(HP_(VL)), or ((HP_(VS))(HP_(ML))²)(HP_(VL)), or (HP_(ML))²/(HP_(VL)), wherein P1 is a product defined byHP_(VS)×HP_(ML), and wherein R1 is a ratio defined by HP_(VL)/HP_(ML).

The test report can include an elevated concentration of HP_(VL),relative to a population norm. The subject can have an elevatedconcentration of HP_(VL) when a respective concentration is ≧80% of apopulation norm.

The report can visually indicate when the subject has (i) elevated highdensity lipoprotein-cholesterol (HDL-C) that is at least one of: ≧60mg/dL, ≧80 mg/dL, or ≧100 mg/dL and (ii) an elevated concentration ofHP_(VL) that is ≧80% of a population norm.

The at least one HDL interaction risk parameter can include P1/R1.

The report can visually indicate that a respective subject is identifiedas at risk for CHD if P1/R1 has a value in the first or second quartileof a population norm.

The report can visually indicate that a respective subject is identifiedas at risk for CHD if P1/R1 has a value ≦170 μmol²/L².

The report can include the at least one HDL interaction risk parameterthat includes at least one of the following: R1, P1, P1/R1, or P1+R1.

The report can include graph that shows whether there is a change in theat least one HDL risk interaction parameter over time to assess a changein CHD risk when HP_(VL) remains above 1.84 μmol/L.

Further features, advantages and details of the present invention willbe appreciated by those of ordinary skill in the art from a reading ofthe figures and the detailed description of the preferred embodimentsthat follow, such description being merely illustrative of the presentinvention. Features described with respect with one embodiment can beincorporated with other embodiments although not specifically discussedtherewith. That is, it is noted that aspects of the invention describedwith respect to one embodiment, may be incorporated in a differentembodiment although not specifically described relative thereto. Thatis, all embodiments and/or features of any embodiment can be combined inany way and/or combination. Applicant reserves the right to change anyoriginally filed claim or file any new claim accordingly, including theright to be able to amend any originally filed claim to depend fromand/or incorporate any feature of any other claim although notoriginally claimed in that manner. The foregoing and other aspects ofthe present invention are explained in detail in the specification setforth below.

As will be appreciated by those of skill in the art in light of thepresent disclosure, embodiments of the present invention may includemethods, systems, apparatus and/or computer program products orcombinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic illustration of lipoprotein subclasses and thechemical shift spectra of a representative sample of lipoproteinconstituent subclasses with a composite plasma signal envelope andsubclass signals that can be used for subclass concentrations.

FIG. 2 is a graph of relative NMR frequency (Hz) versus lipoproteindiameter (nm) of HDL, LDL and VLDL/Chylos for 73 different subclasssignals (for the 73 subpopulations).

FIG. 3 is a schematic illustration of how the 73 subpopulations may begrouped into 9 subclasses to maximize their associations with insulinresistance such as for LP-IR assessments.

FIG. 4 is a schematic illustration of HDL particle size with respect toassociated diameters, relative core volumes and HDL weighting ofparticle subclasses.

FIG. 5 is a graph showing the mean particle concentrations (μmol/L) ofthe 26 HDL subclass components from H1 (smallest) to H26 (largest) formen and women in the MESA study population from an exemplarydeconvolution model according to embodiments of the present invention.

FIG. 6A is a chart showing the varying associations with CHD event riskof different groupings of the 26 HDL subpopulations to make alternatecandidate versions of protective HDL-P according to embodiments of thepresent invention.

FIG. 6B is a graph illustrating CHD risk associations for each of 9different size groupings of the 26 HDL subpopulations and four boxes offurther groupings according to embodiments of the present invention,with the χ2 values from the logistic regression model indicating thestrengths and signs of the CHD risk associations as determined in theMESA study population of 5677 subjects, 285 of whom suffered a CHD eventduring 6 years of follow-up (all 9 subpopulations were included in thesame logistic regression model, adjusted for age, race, smoking, SBP,hypertension medication, BMI, diabetes, LDL-P and log TG) according toembodiments of the present invention.

FIG. 6C is a graph showing the same information as in FIG. 6B butshowing results separately for the female (88 CHD events among 2916participants) and male (197 CHD events among 2761 participants) subjectsin the MESA study population (all 9 subpopulations were included in thesame logistic regression model, adjusted for age, race, smoking, SBP,hypertension medication, BMI, diabetes, LDL-P and log TG).

FIG. 6D is a graph of parameter χ2 of CHD events in MESA (n=289/5710)with the 9 subclasses combined into the four boxed HDL subgroups in FIG.6B (all four HDL subgroups were included in the same logistic regressionmodel, adjusted for age, race, smoking, SBP, hypertension medication,BMI, diabetes, LDL-P and log TG) according to embodiments of the presentinvention.

FIG. 7 is a table of H1-H26 illustrating the HDL subpopulation groupingsand nomenclature including the 9 subpopulations HP1-HP9 shown in FIGS.6B and 6C and the four subclass HDL groupings shown in FIG. 6D selectedfor association with CHD according to embodiments of the presentinvention.

FIG. 8A compares the prediction of CHD events (n=42/1145), as given bythe model χ2 values, of logistic models without (base model) and withdifferent HDL parameters, with analysis restricted to individuals inMESA having elevated concentrations (above the 80^(th) percentile; >1.84μmol/L) of very large HDL particles, H21-H26, according to embodimentsof the present invention.

FIG. 8B is a table containing the same information as FIG. 8A, but withanalysis restricted to a smaller group of individuals (n=23/575) havingeven more elevated concentrations (above the 90^(th) percentile; >2.71μmol/L) of very large HDL particles, H21-H26, according to embodimentsof the present invention. It is noted that this chart is provided by wayof example only as the number of events n=23 make the statisticalconfidence in the results less reliable; additional or larger studypopulations may improve these values.

FIG. 8C is a graph illustrating HDL risk stratification based on thelevel of HP_(VL) according to embodiments of the present invention.

FIG. 9 is a chart of very large HDL-P parameters H21-26 and H23-24illustrating median and interquartile ranges and HR (Hazard Ratio) for289 coronary events from a Cox regression analysis adjusted for thenoted parameters according to embodiments of the present invention.

FIG. 10A is a graph showing associations with CHD events in MESA(n=289/5710), given by the parameter χ2 value, for the two interactionvariables, P1: (HP_(VS)×HP_(ML)) and R1: (HP_(VL)/HP_(ML)), with theformer having a negative risk relationship and the latter a positiverisk relationship. Results are from a logistic regression modelincluding both HDL interaction parameters, adjusted for age, gender,race, smoking, SBP, hypertension medication, BMI, diabetes, LDL-P andlog TG) according to embodiments of the present invention.

FIG. 10B is a graph showing the same information as in FIG. 10A, butwith analysis restricted to the subgroup of individuals (n=42/1145)having elevated concentrations (above the 80^(th) percentile; >1.84μmol/L) of very large HDL particles, H21-H26, according to embodimentsof the present invention.

FIG. 11 is a graph showing the CHD event rate (%), adjusted for age,gender, race, smoking, SBP, hypertension medication, BMI, diabetes,LDL-P and log TG, by quartile of the P1/R1 ratio (which is equal to(HP_(VS)/HP_(VL))(HP_(ML))²) in MESA subjects (n=42/1145) havingHP_(VL)>80^(th) percentile according to embodiments of the presentinvention.

FIG. 12 is a graph similar to that shown in FIG. 11 showing the numberof CHD events by quartile of the P1/R1 ratio in MESA subjects(n=42/1145) having HP_(VL)>80^(th) percentile according to embodimentsof the present invention.

FIG. 13 is a schematic illustration of a system for analyzing CHD riskusing a defined risk parameter comprising a subclass grouping of largeHDL-P (H21-26) according to embodiments of the present invention.

FIG. 14 is a schematic illustration of a NMR spectroscopy apparatusaccording to embodiments of the present invention.

FIG. 15 is a schematic diagram of a data processing system according toembodiments of the present invention.

FIG. 16 is an example of a patient report that includes a protectiveHDL-P number according to embodiments of the present invention.

FIGS. 17A and 17B are examples of graphs that can monitor change in oneor more of very large HDL-P, R1, P1, R1+P1, P1/R1 or R1/P1 and/or HDL-Cover time to evaluate a patient's metabolic status, change, or clinicalefficacy of a therapy or even used for clinical trials and the likeaccording to embodiments of the present invention.

FIG. 18 is a flow chart of exemplary operations that can be used tocarry out embodiments of the present invention.

The foregoing and other objects and aspects of the present invention areexplained in detail in the specification set forth below.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions, layersand/or sections, these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are only usedto distinguish one element, component, region, layer or section fromanother region, layer or section. Thus, a first element, component,region, layer or section discussed below could be termed a secondelement, component, region, layer or section without departing from theteachings of the present invention. The sequence of operations (orsteps) is not limited to the order presented in the claims or figuresunless specifically indicated otherwise.

The term “programmatically” means carried out using computer programdirected operations. The terms “automated” and “automatic” means thatthe operations can be carried out with minimal or no manual labor orinput. The term “semi-automated” refers to allowing operators some inputor activation, but the calculations and signal acquisition as well asthe calculation of the concentrations of the ionized constituent(s) isdone electronically, typically programmatically, without requiringmanual input.

The term “about” refers to +/−10% (mean or average) of a specified valueor number.

The term “circuit” refers to an entirely software embodiment or anembodiment that includes a combination of software and hardwarecomponents. The circuit can be a distributed system using a wirelessand/or internet connection or a system that resides on one apparatus.

The terms CAD and CHD are used interchangeably and broadly to refer to apatient or subject's risk of developing or having coronary artery and/orcoronary heart disease or other negative cardiac event.

The terms “population norm” and “standard” value associated with alipoprotein measurement can be the values defined by a large study suchas the Framingham Offspring Study or the Multi-Ethnic Study ofAtherosclerosis (MESA). However, the instant invention is not limited tothese population values as the presently defined normal or high rangesof at-risk population values (e.g. concentrations or levels) may changeover time.

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

As is generally accepted, HDL-cholesterol and/or LDL-cholesterol levelsprovided by conventional lipid panels fail to sufficiently differentiatepopulations with and without elevated risk for CHD or CAD. As is knownto those of skill in the art, the Framingham study proposed a relativelylengthy risk model that considers many factors such as age, gender,smoking habits, as well as cholesterol values. The research conducted inthe Framingham Offspring Study also defined normative and at-riskpopulation values from subjects in the study. See Wilson et al., Impactof National Guidelines for Cholesterol Risk Factor Screening. TheFramingham Offspring Study, JAMA, 1989; 262: 41-44.

The term “protective HDL-P” refers to HDL-P parameters that have astatistical probability of being inversely associated with risk of CHDand/or providing anti-atherogenic protection against one or more ofatherosclerosis, CHD and/or myocardial infarction (“MI”). The term“NP-HDL” refers to HDL-P parameters which do not provide a statisticalprobability of inverse risk association for one or more ofatherosclerosis, CHD or myocardial infarction (MI). The NP-HDL may bemerely “neutral” as to being associated with an increased risk or may beconsidered to present a positive risk, be atherogenic and/or provide anincreased risk of atherosclerosis, CHD or MI.

Lipoproteins include a wide variety of particles found in plasma, serum,whole blood, and lymph, comprising various types and quantities oftriglycerides, cholesterol, phospholipids, sphyngolipids, and proteins.These various particles permit the solubilization of otherwisehydrophobic lipid molecules in blood and serve a variety of functionsrelated to lipolysis, lipogenesis, and lipid transport between the gut,liver, muscle tissue and adipose tissue.

In blood and/or plasma, HDL has been classified in many ways, generallybased on physical properties such as density or electrophoretic mobilityor measures of apolipoprotein A-1 (Apo A-1), the main protein in HDL.Classification based on nuclear magnetic resonance (NMR) determinedparticle size can distinguish a number of discrete components for eachof VLDL, HDL and LDL subclasses.

The NMR derived estimated HDL-P particle sizes for H1-H26 (FIG. 7) notedherein typically refer to average measurements, but other sizedemarcations may be used.

It is also noted that while NMR measurements of the lipoproteinparticles are contemplated as being particularly suitable for theanalyses described herein, it is contemplated that other technologiesmay be used to measure these parameters now or in the future andembodiments of the invention are not limited to this measurementmethodology. It is also contemplated that different protocols using NMRmay be used (e.g., including different deconvolving protocols) in lieuof the deconvolving protocol described herein. See, e.g., Kaess et al.,The lipoprotein subfraction profile: heritability and identification ofquantitative trait loci, J Lipid Res. Vol. 49 pp. 715-723 (2008); andSuna et al., 1H NMR metabolomics of plasma lipoprotein subclasses:elucidation of metabolic clustering by self-organising maps, NMR Biomed.2007; 20: 658-672. Flotation and ultracentrifugation employing adensity-based separation technique for evaluating lipoprotein particlesand ion mobility analysis are alternative technologies for measuringlipoprotein subclass particle concentrations.

Generally stated, it is believed that accurate CHD risk stratificationmay depend on recognizing that there is a fundamentally differentrelationship of HDL with CHD risk depending on whether levels of thevery large HDL subclass (HP_(VL)) are elevated or not. A subset ofpatients with elevated levels of HP_(VL) have high CHD risk, possiblybecause their HDL particles are dysfunctional and/or proatherogenic.Identification of this high-risk subset can be made by calculating therelative amounts of HP_(VL) and other defined HDL particlesubpopulations.

As shown in FIGS. 1 and 2, over 20 discrete subpopulations (sizes) oflipoprotein particles, typically between about 30-80 different sizesubpopulations (or even more) can be measured for a blood plasma orserum sample. These discrete sub-populations can be grouped into definedsubclasses. The defined subclasses can include a plurality of differentsubclasses, including three or more for each of VLDL and HDL and two orthree or more for LDL (if the latter, with one of the three identifiedas IDL in the size range between large LDL and small VLDL).

FIG. 3 illustrates that conventional CHD risk assessment involves LDL-Pand HDL-P numbers and that insulin resistance/diabetes prediction orrisk is associated with concentrations of large VLDL, small LDL andlarge HDL.

FIG. 4 is a schematic illustration showing that because larger HDLparticles contain so much more cholesterol than smaller HDL particles,HDL-C as a risk biomarker gives much more weight to variations in theconcentration of the large subpopulations and relatively undervaluesdifferences in concentration of the smaller size subpopulations.

Embodiments of the invention classify lipoprotein particles intosubclasses grouped by size ranges based on functional/metabolicrelatedness as assessed by their correlations to CHD risk as shown inFIG. 6A-6D, for example.

HDL-P sizes typically range (on average) from between about 7 nm toabout 15 nm, more typically about 7.3 nm to about 14 nm. The HDLsubclasses of different size can be quantified from the amplitudes oftheir spectroscopically distinct lipid methyl group NMR signals. See,Jeyarajah et al., Lipoprotein particle analysis by nuclear magneticresonance spectroscopy, Clin Lab Med. 2006; 26: pp. 847-870, thecontents of which are hereby incorporated by reference as if recited infull herein. The HDL-P concentration is the sum of the particleconcentrations of all of the respective HDL subpopulations.

The term “very small” HDL particle subclasses (HP_(VS)) refers to HDLparticle subclasses or HDL subpopulations with estimated (average)diameter sizes <7.6 nm, typically 7.4 nm≦HP_(VS)<7.6 nm.

The term “very large” HDL particle subclasses (HP_(VL)) refers to HDLparticle subclasses or HDL subpopulations with estimated (average)diameter sizes ≧11.0 nm, typically between 13.5 nm≦HP_(VL)≦11.0 nm.

The term “medium plus large” HDL particle subclasses (HP_(ML)) refers toHDL particle subclasses or HDL subpopulations with estimated (average)diameter sizes between 8.3 nm and 10.9 nm or 10.8 nm; typically 8.3nm≦HP_(ML)≦10.9 nm.

The term “small” HDL particle subclasses (HPs) refers to HDL particlesubclasses or HDL subpopulations with estimated (average) diameter sizesbetween 7.6 to 8.2 nm.

It is contemplated that the defined estimated ranges for one or more ofHP_(VS), HP_(S), HP_(VL), or HP_(ML) may vary by +/−0.1 nm or somewhatmore, particularly when measured with alternative NMR deconvolving orother methods, without unduly affecting a statistical risk associationand/or a subclass grouping.

The term “HDL interaction risk parameter” refers to a parameter thatincludes two or more defined subclasses of HDL that are combined into asingle component/parameter and define a positive or negative statisticalrisk association based on a logistic regression model.

The term “CHD risk model” refers to a statistical risk model havingdefined parameters associated with a likelihood of having or developingCHD risk as measured by standard χ2 and/or p values (the latter with asufficiently representative study population). The CHD risk model can bea logistic regression model.

The term “P1” refers to the product of HP_(VS)×HP_(ML) as an HDLinteraction risk parameter.

The term “R1” refers to a ratio of HP_(VL)/HP_(ML) as an HDL interactionrisk parameter.

LDL is known to carry the so-called “bad” cholesterol. LDL particlescome in different sizes. Conventionally, the smaller sizes have beenthought to be the most dangerous type in that they were generallythought to be inherently more atherogenic than large particles. See,Sacks et al., Clinical review 163: Cardiovascular endocrinology: Lowdensity lipoprotein size and cardiovascular disease: a reappraisal, J.Clin. Endocrinol Metab., 2003; 88: 4525-4532. Presently, LDL particlesizes are characterized as “Pattern A” (large) and “Pattern B” (small).Pattern A can be defined as large average particle sizes which typicallyincludes sizes of between about 20.5-23.0 nm. Pattern B can be definedas smaller average particle sizes between about 18.0-20.5 nm. The LDL-Pnumber can be defined as the sum of the small, large and IDL subclassconcentrations (FIG. 3). As shown in FIGS. 3 and 4, the small LDLparticles can include particles whose sizes range from between about18.0 to about 20.5 nm. The large LDL particles can include particlesranging in diameter between about 20.5-23.0 nm. It is noted that the LDLsubclasses of particles can be divided in other size ranges. Inaddition, intermediate-density lipoprotein particles (“IDL” or “IDL-P”),which range in diameter from approximately 23.0-29.0 nm, can be includedamong the particles defined as LDL.

As shown in the chart of FIG. 6A, when the particle concentrations ofall 26 of the HDL subpopulations are added together, as indicated by theshaded squares in the top row of the chart, it produces total HDL-P, themean concentration of which is 33.7 μmol/L in the MESA study population.By omitting from total HDL-P the concentrations of the varioussubpopulations indicated by the non-shaded squares in the chart, 12other groupings of HDL-P are generated. The incremental amount of CHDrisk prediction given by each of these alternate versions of HDL-P wasassessed in logistic regression models adjusted for 8 non-lipidcovariates and LDL-P and triglycerides (TG). The χ2 values in theleft-most column of the chart give a quantitative assessment of how muchincremental prediction was given by inclusion of each of the alternateHDL-P parameters in the regression model. Unexpectedly, it was foundthat omitting the 6 largest HDL subpopulations (H21-H26) made the HDL-Pparameter more predictive of CHD risk, as indicated by the increase ofthe χ2 value to 3.46. As shown in the bottom section of the chart, aneven more robust association of the HDL-P parameter was obtained byadditionally omitting the H8 subpopulation. Stated differently, in someembodiments, the “protective HDL-P” number can exclude HDL subclasscomponents at or above about 11 nm, e.g., with sizes between about 13.5nm to about 11 nm and/or the non-protective HDL-P are the very largeHDL-P subpopulations H21-26.

FIG. 9 is a chart that identifies potential NP HDL-P parameters(“A-HDL-Ps”). When comparing two groupings of large HDL subclasscomponents, H26-H21 versus H24-H23, both show positive risk associationswith CHD. A risk assessment can be generated that considers both NPHDL-P and protective HDL-P.

As shown in FIGS. 6B and 6C, for example, in some embodiments, the totalnumber of subpopulations in the HDL subclasses can be grouped into ninesubgroups, HP1-HP9, which can then be further grouped into fourgroupings shown by the four boxes in FIGS. 6B and 6C. The “four”subclass groupings selected for CHD risk association can be described asHP_(VS) (H1-H2), HP_(S) (H3-H8), HP_(ML)(H9-20) and HP_(VL) (H21-26).FIG. 6C illustrates the gender differences for the CHD riskrelationships of the 9 subgroups and the four groups relative to the“composite” graph shown in FIG. 6B.

FIG. 6D is a graph illustrating risk associations, given by the χ2parameter, for each of the four subclass groups HP_(VS), HP_(S),HP_(ML), HP_(VL). The HP_(S) subclass association is negative but iscloser to a neutral risk association relative to the HP_(VS) or HP_(ML)subclasses. While the HDL risk interaction parameters described hereinwere configured to exclude HP_(S), the HP_(S) subclass could be includedas well. However, this can add computational complexity that does notappear warranted based on the risk stratification provided by the otherthree subclass groups.

FIG. 7 is a chart of the 26 different HDL subpopulations, 9 subgroupingsand four subclass groups HP_(VS), HP_(S), HP_(ML), HP_(VL) whichprovides the nomenclature and size (average) ranges of the HDLsubpopulations according to some embodiments of the present invention.As noted above, the four subclass groups are selected based on astatistical analysis of epidemiologic associations to determine how thevarious subpopulations should be grouped based on risk association withCHD (rather than LP-IR or insulin resistance or diabetes as described,for example, in U.S. Pat. No. 8,386,187, the content of which is herebyincorporated by reference as if recited in full herein).

FIGS. 8A and 8B are tables illustrating prediction of CHD events in MESAusing only data associated with those people having high levels ofHP_(VL). The table compares risk prediction given by a base logisticregression model and models also including the different interactionterms, P1, R1, P1+R1 and P1/R1. The base model included the following 10covariates: age, gender, race, smoking, SBP, hypertension medication,BMI, diabetes, LDL-P and log TG.

FIG. 8A includes data from MESA subjects (n=1145; 42 CHD events) withhigh concentrations of HP_(VL)>80^(th) percentile (above 1.84 μmol/L).

FIG. 8B includes data from MESA subjects (n=575; 23 CHD events) witheven higher concentrations of HP_(VL)>90% (above 2.71 μmol/L) accordingto embodiments of the present invention. It is noted that FIG. 8B isprovided by way of example only as the relatively small number of events(n=23) make the statistical confidence in the results for the R1 and P1and interaction components less reliable; additional or larger studypopulations may improve the reliability of these values.

As shown conceptually in FIG. 8C, if the patient's level of HP_(VL) isnot high (for example, <80^(th) percentile, or some other appropriatevalue which may differ from this exemplary threshold), CHD risk can besuitably stratified using quartiles of HDL-P. Alternatively, tertiles,quintiles, or other segmentation of a population can be used. It is alsocontemplated that instead of HDL-P another NMR HDL parameter could beused such as (HDL-P)², HP_(ML), or (HP_(ML))².

On the other hand, if the patient's level of HP_(VL) is high or elevated(e.g., above a defined threshold or range), CHD risk is better (andpotentially optimally) stratified by taking account of the relativeamounts of three of the four previously discussed subgroups such asthose described in FIGS. 8A and 8B: HP_(VL), HP_(ML), and HP_(VS).

In some embodiments, the HDL risk parameter combines P1 and R1 as aratio, P1/R1.

When combined as P1/R1, the HDL interaction risk parameter isrepresented by the below equation:(HP _(VS) /HP _(VL))(HP _(ML))²  Equation (1)

In this form, the numerator and the denominator of the first term(HP_(VS)/HP_(VL)) are both relatively small values (see, FIG. 5) whichin a general population may not be sufficiently discriminating (or thedenominator may be zero for part of the population) but when directed atthe subpopulation of people having high levels of HP_(VL), can be auseful risk predictor.

As shown by the right side of the graph in FIG. 8C, when a person haselevated HP_(VL), the CHD risk may be predicted relative to a combinedinteraction parameter relative to values associated with a definedsegment of the population, e.g., shown as the first quartile or thefirst and second quartiles (note other population segments can be used,e.g., tertiles, quintiles, and the like) of the HDL interactionparameter such as Equation (1).

Alternative HDL interaction parameters may also or additionally be usedsuch as those shown in FIGS. 8A and 8B, for example.

In some embodiments, the HDL interaction parameter can be as shown inEquation (2) or Equation (3).(HP _(VS))(HP _(ML))/(HP _(VL))  Equation (2)(HP _(ML))²/(HP _(VL))  Equation (3)

It is contemplated that one or both of the new HDL interaction riskparameter(s) which can include, for example, R1, P1 alone or combined inany manner including the combinations shown in Equations (1)-(3) can beused as a marker for identifying those patients having elevated HP_(VL)that are at increased risk of CHD.

In some embodiments, a respective subject can be identified as at riskfor CHD if P1/R1 has a value ≦170 μmol²/L².

HDL has been associated with a number of different functions including,for example, promoting cholesterol efflux, anti-inflammation,antioxidation, increasing nitrous oxide production, protecting againstlipopolysaccharide, vasoprotective, antifibrotic, and anthithrombotic.While the identification may be sufficient to make therapy or riskmanagement decisions on its own, it is contemplated that in someembodiments, additional tests may be carried out on these patients toidentify a particular dysfunction(s) to allow for better therapydecisions. Examples of such companion or further assays that may becarried out include, but are not limited to those described in one ormore of the following: U.S. Pat. No. 7,723,045 to Fogelman et al., U.S.Pat. No. 7,250,304 to Fogelman et al., U.S. 2011/0124031 to Hazen etal., U.S. Pat. No. 7,771,954 to Hazen et al.; U.S. 2011/0201947 to Hazenet al., U.S. 2010/0285517 to Hazen et al., and U.S. 2005/0244892 toRader et al., the contents of which are hereby incorporated by referenceas if recited in full herein.

Different therapies that increase HDL-C by the same amount may notincrease the HDL subclasses proportionately. Some drugs, for example,increase HDL-C mainly by increasing the number of small HDL particles(such as those in the fibrate class). Others increase mainly largeHDL-P. The HDL particle subclass concentrations can changedifferentially with different therapies, indicating potentially greateror lesser clinical benefit and may provide enhanced protocols forevaluating therapeutic efficacy. See, e.g., Rashedi N, Brennan D,Kastelein J J, Nissen S E, Nicholls S. 2011 European AtherosclerosisSociety meeting presentation (providing a graph illustrating the impactof a CETP inhibitor (torceptrapib) on NMR-derived lipoprotein particleparameters.

Embodiments of the invention provide an automated analysis and/or reportof a blood or plasma sample of a mammalian patient (typically a human)that identifies a likelihood of increased CHD risk for patients havinghigh levels of HD_(VL). An HDL therapy can be adjusted, monitored orselected based on the CHD risk identification. Thus, the CHD risk usingthe HDL interaction risk parameter (e.g., R1 and/or P1) when a patienthas elevated very large HDL-P may can be used as a risk assessmentand/or therapy management tool.

FIG. 10A is a graph of parameter χ2 showing associations of CHD eventsin MESA (n=289/5710) for P1 (HP_(VS)×HP_(ML)) and R1 (HP_(VL)/HP_(ML))HDL interaction risk components (one having a positive risk associationand one having a negative risk association) included in the samelogistic regression model, adjusted for age, gender, race, smoking, SBP,hypertension medication, BMI, diabetes, LDL-P and log TG according toembodiments of the present invention. The model χ2 increased more thandouble with two subclass interactions.

FIG. 10B is a graph of parameter χ2 showing associations of CHD eventsin MESA (n=42/1145) in a population subgroup having high HP_(VL) for P1(HP_(VS)×HP_(ML)) and R1 (HP_(VL)/HP_(ML)). This graph is similar tothat shown in FIG. 10A. As for FIG. 10A, the P1 HDL interactioncomponent has negative association and the R1 interaction component hasa positive risk association with CHD. The two HDL interaction parametersor terms were included in the same logistic regression model, adjustedfor age, gender, race, smoking, SBP, hypertension medication, BMI,diabetes, LDL-P and log TG) according to embodiments of the presentinvention.

FIG. 11 is a graph showing the adjusted CHD event rates of individualsdivided into quartiles according to the P1/R1 ratio in MESA subjects(n=42/1145) having HP_(VL)>80^(th) percentile according to embodimentsof the present invention.

FIG. 12 is a graph similar to that shown in FIG. 11 showing the numbers80^(th) of CHD events in each P1/R1 quartile in MESA subjects(n=42/1145) having HP_(VL)>80^(th) percentile according to embodimentsof the present invention.

Referring now to FIG. 13, it is contemplated that the protective (andoptionally non-protective) HDL-P analysis can be carried out using asystem 10 with an NMR clinical analyzer 22 as described, for example,with respect to FIG. 14 below and/or in U.S. Pat. No. 8,013,602, thecontents of which are hereby incorporated by reference as if recited infull herein.

The system 10 can include a CHD risk analysis circuit for elevated largeHDL-P 20 that can be on-board the analyzer 22 or remote from theanalyzer 22. If the latter, the analysis module or circuit 20 can residetotally or partially on a server 150. The server 150 can be providedusing cloud computing which includes the provision of computationalresources on demand via a computer network. The resources can beembodied as various infrastructure services (e.g. compute, storage,etc.) as well as applications, databases, file services, email, etc. Inthe traditional model of computing, both data and software are typicallyfully contained on the user's computer; in cloud computing, the user'scomputer may contain little software or data (perhaps an operatingsystem and/or web browser), and may serve as little more than a displayterminal for processes occurring on a network of external computers. Acloud computing service (or an aggregation of multiple cloud resources)may be generally referred to as the “Cloud”. Cloud storage may include amodel of networked computer data storage where data is stored onmultiple virtual servers, rather than being hosted on one or morededicated servers. Data transfer can be encrypted and can be done viathe Internet using any appropriate firewalls to comply with industry orregulatory standards such as HIPAA. The term “HIPAA” refers to theUnited States laws defined by the Health Insurance Portability andAccountability Act. The patient data can include an accession number oridentifier, gender, age and test data.

The results of the analysis can be transmitted via a computer network,such as the Internet, via email or the like to a clinician site 50, to ahealth insurance agency 52 or a pharmacy 51. The results can be sentdirectly from the analysis site or may be sent indirectly. The resultsmay be printed out and sent via conventional mail. This information canalso be transmitted to pharmacies and/or medical insurance companies, oreven patients that monitor for prescriptions or drug use that may resultin an increase risk of an adverse event. The results can be sent to apatient via email to a “home” computer or to a pervasive computingdevice such as a smart phone or notepad and the like. The results can beas an email attachment of the overall report or as a text message alert,for example.

Referring now to FIG. 14, a system 207 for acquiring and calculating thelineshape of a selected sample is illustrated. The system 207 includesan NMR spectrometer 22 for taking NMR measurements of a sample. In oneembodiment, the spectrometer 22 is configured so that the NMRmeasurements are conducted at 400 MHz for proton signals; in otherembodiments the measurements may be carried out at 360 MHz or othersuitable frequency. Other frequencies corresponding to a desiredoperational magnetic field strength may also be employed, typicallybetween about 200 MHz-900 MHz. Typically, a proton flow probe isinstalled, as is a temperature controller to maintain the sampletemperature at 47+/−0.5 degrees C. The spectrometer 22 is controlled bya digital computer 214 or other signal processing unit. The computer 211should be capable of performing rapid Fourier transformations. It mayalso include a data link 212 to another processor or computer 213, and adirect-memory-access channel 214 which can connects to a hard memorystorage unit 215.

The digital computer 211 may also include a set of analog-to-digitalconverters, digital-to-analog converters and slow device I/O ports whichconnect through a pulse control and interface circuit 216 to theoperating elements of the spectrometer. These elements include an RFtransmitter 217 which produces an RF excitation pulse of the duration,frequency and magnitude directed by the digital computer 211, and an RFpower amplifier 218 which amplifies the pulse and couples it to the RFtransmit coil 219 that surrounds sample cell 220. The NMR signalproduced by the excited sample in the presence of a 9.4 Tesla polarizingmagnetic field produced by superconducting magnet 221 is received by acoil 222 and applied to an RF receiver 223. The amplified and filteredNMR signal is demodulated at 224 and the resulting quadrature signalsare applied to the interface circuit 216 where they are digitized andinput through the digital computer 211. The lipoprotein measurementand/or protective HDL-P analyzer circuit 20 or module 350 (FIGS. 13-15)or circuit 20 can be located in one or more processors associated withthe digital computer 211 and/or in a secondary computer 213 or othercomputers that may be on-site or remote, accessible via a worldwidenetwork such as the Internet 227.

After the NMR data are acquired from the sample in the measurement cell220, processing by the computer 211 produces another file that can, asdesired, be stored in the storage 215. This second file is a digitalrepresentation of the chemical shift spectrum and it is subsequentlyread out to the computer 213 for storage in its storage 225 or adatabase associated with one or more servers. Under the direction of aprogram stored in its memory, the computer 213, which may be a personal,laptop, desktop, workstation, notepad or other computer, processes thechemical shift spectrum in accordance with the teachings of the presentinvention to generate a report which may be output to a printer 226 orelectronically stored and relayed to a desired email address or URL.Those skilled in this art will recognize that other output devices, suchas a computer display screen, notepad, smart phone and the like, mayalso be employed for the display of results.

It should be apparent to those skilled in the art that the functionsperformed by the computer 213 and its separate storage 225 may also beincorporated into the functions performed by the spectrometer's digitalcomputer 211. In such case, the printer 226 may be connected directly tothe digital computer 211. Other interfaces and output devices may alsobe employed, as are well-known to those skilled in this art.

Certain embodiments of the present invention are directed at providingmethods, systems and/or computer program products that evaluate elevatedlevels of large HDL-P numbers to identify those people at an increasedrisk of having or developing CHD that may be particularly useful inautomated screening tests and/or risk assessment evaluations for CHDscreening of in vitro biosamples.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as an apparatus, a method, data or signal processingsystem, or computer program product. Accordingly, the present inventionmay take the form of an entirely software embodiment, or an embodimentcombining software and hardware aspects. Furthermore, certainembodiments of the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputer-usable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

The computer-usable or computer-readable medium may be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, device, or propagation medium. Morespecific examples (a nonexhaustive list) of the computer-readable mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, and a portable compact discread-only memory (CD-ROM). Note that the computer-usable orcomputer-readable medium could even be paper or another suitable medium,upon which the program is printed, as the program can be electronicallycaptured, via, for instance, optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java7, Smalltalk, Python, Labview, C++, or VisualBasic. However, thecomputer program code for carrying out operations of the presentinvention may also be written in conventional procedural programminglanguages, such as the “C” programming language or even assemblylanguage. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

FIG. 15 is a block diagram of exemplary embodiments of data processingsystems that illustrates systems, methods, and computer program productsin accordance with embodiments of the present invention. The processor310 communicates with the memory 314 via an address/data bus 348. Theprocessor 310 can be any commercially available or custommicroprocessor. The memory 314 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 305. Thememory 314 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 15, the memory 314 may include several categories ofsoftware and data used in the data processing system 305: the operatingsystem 352; the application programs 354; the input/output (I/O) devicedrivers 358; a CHD risk stratification for High Levels of HP_(VL) Module350; and the data 356. The Module 350 can sum concentrations of definedsubpopulations of HDL to determine if an elevated HPVL condition exitsthen calculate an HDL interaction risk parameter using different definedsubpopulations of the HDL.

The data 356 may include signal (constituent and/or composite spectrumlineshape) data 362 which may be obtained from a data or signalacquisition system 320. As will be appreciated by those of skill in theart, the operating system 352 may be any operating system suitable foruse with a data processing system, such as OS/2, AIX or OS/390 fromInternational Business Machines Corporation, Armonk, N.Y., WindowsCE,WindowsNT, Windows95, Windows98, Windows2000 or WindowsXP from MicrosoftCorporation, Redmond, Wash., PalmOS from Palm, Inc., MacOS from AppleComputer, UNIX, FreeBSD, or Linux, proprietary operating systems ordedicated operating systems, for example, for embedded data processingsystems.

The I/O device drivers 358 typically include software routines accessedthrough the operating system 352 by the application programs 354 tocommunicate with devices such as I/O data port(s), data storage 356 andcertain memory 314 components and/or the image acquisition system 320.The application programs 354 are illustrative of the programs thatimplement the various features of the data processing system 305 and caninclude at least one application, which supports operations according toembodiments of the present invention. Finally, the data 356 representsthe static and dynamic data used by the application programs 354, theoperating system 352, the I/O device drivers 358, and other softwareprograms that may reside in the memory 314.

While the present invention is illustrated, for example, with referenceto the Module 350 being an application program in FIG. 15, as will beappreciated by those of skill in the art, other configurations may alsobe utilized while still benefiting from the teachings of the presentinvention. For example, the protective HDL-P Module 350 may also beincorporated into the operating system 352, the I/O device drivers 358or other such logical division of the data processing system 305. Thus,the present invention should not be construed as limited to theconfiguration of FIG. 15, which is intended to encompass anyconfiguration capable of carrying out the operations described herein.

In certain embodiments, the Module 350 includes computer program may beused to indicate whether therapy intervention is desired and/or trackefficacy of a therapy.

FIG. 16 is a schematic illustration of an exemplary patient test report100 that can include various lipoprotein parameters such as LDL-P, VLDLand a CHD risk alert 100R based on a calculated HDL interaction riskparameter (shown as both R1 and P1/R1 for example) when a patient haselevated concentrations of (very) large HDL-P. The HDL interaction risknumber can be presented with a risk assessment data correlated topopulation norms, typical ranges, and/or degree of risk.

FIG. 17A illustrates that a graph of various HDL parameters can begenerated including, for example, one or more of (very) large HDL-P,HDL-C, protective and NP HDL-P can be provided to illustrate a change inpatient metabolic HDL function over time due to age, medicalintervention or a therapy according to some embodiments.

As shown in FIG. 17B, R1 and/or P1/R1 (or another HDL risk interaction)along with the level of HP_(VL) can be tracked using a graph or dataover time to monitor a patient over time to correlate known start or useof a drug or other therapy and/or to evaluate whether HDL function hasbeen altered and/or whether protective (or non-protective) HDL-P hasbeen increased or decreased using such therapy.

Tracking of one or more of these parameters may provide better clinicalindicators of efficacy of a therapy and/or a better risk predictor forCHD for patients.

Future drugs or uses of known drugs can be identified, screened ortested in patients identified using the HDL-P evaluations.

FIG. 18 is a flow chart of exemplary operations that can be used tocarry out embodiments of the invention for determining protective highdensity lipoprotein particle (HDL-P) numbers. Concentration measurementsof subpopulations of HDL-P subclasses in a blood plasma or serum samplecan be obtained (block 500). The number of subpopulations can vary buttypically include at least 20, such as about 26. CHD risk stratificationcan be evaluated using a ratio or product of defined subsets of the HDLsubpopulations, e.g., at least one of R1 or P1 (block 510).

The concentrations can be via NMR, flotation and ultracentrifugation orion mobility, for example (block 502).

Twenty six subpopulation concentrations can be obtained, correspondingto H1 to H26, where HP_(VL)=H21-26 (e.g., particles within about 14 nmor 13.5 nm to 11 nm), HP_(VS)=H11+H2 (e.g., particles within about 7.4nm-7.5 nm), and HP_(ML)=H9-H20 (particles within about 8.3-10.9 nm)(block 508).

Optionally, a CHD risk based non-protective HDL number (H21-H26) and/ora protective HDL number can be calculated using one or more of: H1-H2,H-3-H8, and H-9-H20) (block 505).

It is contemplated that there can be at least two different defined CHDrisk tests, one for those people with low or normal levels of very largeHDL-P (HP_(VL)) and one for those with elevated levels of very largeHDL-P (HP_(VL)). The former can use the HDL-P number while the lattercan have a modified test that considers the HDL risk interactionparameter.

In other embodiments, a standard test can be performed on all samplesand a secondary test on those with the elevated very large HDL-P(HP_(VL)).

For laboratories performing only HDL-C, where a person has high levelsof HDL-C, they can be referred or a sample processed to see if there areelevated levels of very large HDL-P (HP_(VL)).

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. In the claims, means-plus-function clauses, where used, areintended to cover the structures described herein as performing therecited function and not only structural equivalents but also equivalentstructures. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

That which is claimed is:
 1. A method of determining whether a subjectwith elevated concentrations of very large high density lipoprotein(HDL) particles (HDL-P) is at increased risk for a cardiac event and/orCHD, comprising: providing a blood plasma or serum sample from thesubject; measuring, using a NMR spectrometer; HP_(VS), HP_(ML), andHP_(VL), where HP_(VS) is a concentration of very small HDL-Psubclasses, HP_(ML) is a concentration of medium and large HDL-Psubclasses, and HP_(VL), is a concentration of very large HDL-Psubclasses, programmatically calculating at least one HDL interactionrisk parameter associated with HDL content of the blood plasma or serumsample of the subject, the at least one HDL interaction risk parametercomprising P1 or R1, or P1 and R1, wherein P1 is a product defined byHP_(VS) times HP_(ML), and wherein R1 is a ratio defined byHP_(VL)/HP_(ML), and determining the risk for a cardiac event and/or CHDusing the at least one HDL interaction risk parameter.
 2. The method ofclaim 1, wherein the at least one HDL interaction risk parameter is atleast one of the following: (HP_(VS))(HP_(ML))/(HP_(VL)), or((HP_(VS))(HP_(ML)) ²)/(HP_(VL)), or (HP_(ML))²/(HP_(VL)).
 3. The methodof claim 1, further comprising: electronically identifying when asubject has an elevated concentration of very large HDL-P (HP_(VL))relative to a population norm, wherein the subject has an elevatedconcentration of HP_(VL) when the subject's concentration is ≧80% of apopulation norm, and wherein the programmatic calculation of at leastone HDL interaction risk parameter is electronically selectively carriedout only when the subject has the elevated concentration of HP_(VL). 4.The method of claim 1, wherein the HP_(VS) includes HDL subpopulationshaving an average diameter between 7.4 nm and 7.6 nm.
 5. The method ofclaim 1, wherein the HP_(ML) includes HDL subpopulations having anaverage diameter between 8.3 nm and 10.9 nm.
 6. The method of claim 1,wherein the HP_(VL) includes HDL subpopulations having an averagediameter between 11 nm and 13.5 nm.
 7. The method of claim 1, furthercomprising: deconvolving an NMR composite signal into 26 subpopulations(H1-H26) of different sizes of HDL-P ranging from a smallest HDL-P sizeassociated with H1 to a largest HDL-P size associated with H26;electronically calculating concentrations of H1 and H2 to generate theHP_(VS); electronically calculating concentrations of H9-H20 to generatethe HP_(ML); and electronically calculating concentrations of H21 andH26 to generate the HP_(VL).
 8. The method of claim 1, wherein theprogrammatically calculated at least one HDL interaction risk parameterincludes P1/R1.
 9. The method of claim 8, wherein a subject isidentified as at risk for CHD if P1/R1 for the subject has a value inthe first or second quartile of a population norm.
 10. The method ofclaim 8, wherein a subject is identified as at risk for CHD if P1/R1 forthe subject has a value ≦170 μmol²/L².
 11. The method of claim 1,further comprising generating a report that visually and/or textuallyindicates whether the subject is at increased risk of CHD despite havingelevated HP_(VL).
 12. The method of claim 1, further comprisinggenerating a report based on the programmatic calculation of the atleast one HDL interaction risk parameter that includes at least one ofthe following: R1, P1, P1/R1, or P1+R1.
 13. The method of claim 1,further comprising electronically monitoring whether there is a changein the HDL risk interaction parameter over time to assess a change inCHD risk when HP_(VL) remains above 1.84 μmol/L.
 14. The method of claim1, further comprising referring the subject for further medicalevaluation if the programmatic calculation indicates there is alikelihood of increased risk of CHD despite an elevated very largeHDL-P.
 15. The method of claim 1, wherein the subject is mammalian andwherein the obtaining and calculating steps are carried out using atleast one processor, the method further comprising providing a reportindicating whether the subject is at risk of having and/or developingCHD based, in part, on the programmatic calculation.
 16. The method ofclaim 1, further comprising monitoring the at least one HDL interactionrisk parameter for a change in the HDL risk interaction parameter overtime.
 17. The method of claim 16, wherein the monitoring comprises acomputer program used to indicate whether therapy intervention isdesired and/or track efficacy of a therapy.
 18. A method of determiningwhether a subject is at increased risk for a cardiac event and/or CHD,comprising: (a) providing a blood plasma or serum sample from thesubject, wherein the subject has elevated high densitylipoprotein-cholesterol (HDL-C) that is at least one of: ≧60 mg/dL, ≧80mg/dL, or ≧100 mg/dL; (b) measuring by a NMR spectrometer concentrationsfor HP_(VS), HP_(ML) and HP_(VL), where HP_(VS) is a concentration ofvery small HDL-P subclasses, HP_(ML) is a concentration of medium andlarge HDL-P subclasses, and HP_(VL) is a concentration of very largeHDL-P subclasses, (c) electronically identifying whether the subjectalso has an elevated concentration of HP_(VL) that is ≧80% of apopulation norm, (d) programmatically calculating at least one HDLinteraction risk parameter associated with HDL content of the bloodplasma or serum sample of the subject, the at least one HDL interactionrisk parameter comprising at least two of HP_(VS), HP_(ML), and HP_(VL)if the subject has the elevated concentration of HP_(VL) that is ≧80% ofa population norm, and (e) determining whether the subject is atincreased risk using the at least one HDL interaction risk parameter.19. A computer program product for stratifying CHD risk for patientswith elevated concentrations of very large high density lipoprotein(HDL) particles (HDL-P), the computer program product comprising: anon-transitory computer readable storage medium having computer readableprogram code embodied in the medium, the computer-readable program codecomprising: computer readable program code that obtains NMR signal dataof an in vitro blood plasma or serum sample of a subject to determineNMR-derived concentration measurements of at least twenty subpopulationsof HDL-P subclasses in a blood plasma or serum sample; computer readableprogram code for electronically identifying when the subject has anelevated concentration of HP_(VL) relative to a population norm, whereinthe subject has an elevated concentration of HP_(VL) when the subject'sHP_(VL) concentration is ≧80% of a population norm; and computerreadable program code that calculates at least one HDL interaction riskparameter associated with HDL content of a blood plasma or serum sampleof the subject, the at least one HDL interaction risk parametercomprising at least two of HP_(VS), HP_(ML), and HP_(VL), where HP_(VS)is a concentration of very small HDL-P subclasses, HP_(ML) is aconcentration of medium and large HDL-P subclasses, and HP_(VL) is aconcentration of very large HDL-P subclasses, wherein the at least oneHDL interaction risk parameter includes P1 or R1, or P1 and R1, whereinP1 is a product defined by HP_(VS)×HP_(ML), and wherein R1 is a ratiodefined by HP_(VL)/HP_(ML), and computer readable program code thatdetermines the risk of CHD for the subject using the at least one HDLinteraction risk parameter.
 20. The computer program product of claim19, further comprising computer readable program code that screenssubjects that may benefit from an HDL risk stratification test by (a)first identifying if the subject has elevated high densitylipoprotein-cholesterol (HDL-C) that is at least one of: ≧60 mg/dL, ≧80mg/dL, or ≧100 mg/dL; then (b) identifying when the subject also has anelevated concentration of HP_(VL) that is ≧80% of a population norm,then directing the calculation if the subject has the elevatedconcentration of HP_(VL).
 21. The computer program product of claim 19,wherein the HP_(VS) includes HDL subpopulations having an averagediameter between 7.4 nm and 7.6 nm, wherein the HP_(ML) includes HDLsubpopulations having an average diameter between 8.3 nm and 10.9 nm,and wherein the HP_(VL) includes HDL subpopulations having an averagediameter between 11 nm and 13.5 nm.
 22. The computer program product ofclaim 19, further comprising computer readable program code configuredto: deconvolve an NMR composite signal into 26 subpopulations (H1-H26)of different sizes of HDL-P ranging from a smallest HDL-P sizeassociated with H1 to a largest HDL-P size associated with H26;calculate concentrations of H1 and H2 to generate the HP_(VS); calculateconcentrations of H9-H20 to generate the HP_(ML); and calculateconcentrations of H21 and H26 to generate the HP_(VL).
 23. The computerprogram product of claim 19, wherein the at least one HDL interactionrisk parameter includes P1/R1.
 24. The computer program product of claim23, wherein the subject is identified as at risk for CHD if P1/R1 has avalue in the first or second quartile of a population norm.
 25. Thecomputer program product of claim 23, wherein the subject is identifiedas at risk for CHD if P1/R1 has a value ≦170 μmol²/L².
 26. The computerprogram product of claim 19, further comprising generating a report thatvisually and/or textually indicates whether the subject is at increasedrisk of CHD despite having elevated HP_(VL).
 27. The computer programproduct of claim 19, further comprising computer readable program codeconfigured to electronically provide data representing, generate a graphshowing, and/or monitor whether there is a change in the HDL riskinteraction parameter for the subject over time to assess a change inCHD risk when HP_(VL) remains above 1.84 μmol/L.
 28. The computerprogram product of claim 19, wherein the subject is mammalian, theproduct further comprising computer readable program code that obtainsNMR signal data of an in vitro blood plasma or serum sample of thesubject to determine NMR derived concentration measurements, andcomputer readable code that provides a report indicating whether thesubject is at risk of having and/or developing CHD based, in part, onthe at least one HDL interaction risk parameter.
 29. A system foranalyzing CHD risk, comprising: a circuit comprising at least oneprocessor configured to determine whether a subject with elevatedconcentrations of very large high density lipoprotein (HDL) particles(HDL-P) is at increased risk for a cardiac event and/or CHD, the atleast one processor configured to obtain NMR signal data of an in vitroblood plasma or serum sample of the subject, determine NMR-derivedconcentration measurements for HP_(VS), HP_(ML), and HP_(VL), whereHP_(VS) is a concentration of very small HDL-P subclasses, HP_(ML) is aconcentration of medium and large HDL-P subclasses, and HP_(VL) is aconcentration of very large HDL-P subclasses, and calculate at least oneHDL interaction risk parameter associated with HDL content of a bloodplasma or serum sample of the subject, the at least one HDL interactionrisk parameter comprising P1 or R1, or P1 and R1, wherein P1 is aproduct defined by HP_(VS) times HP_(ML), and wherein R1 is a ratiodefined by HP_(VL)/HP_(ML).
 30. The system of claim 29, wherein thecircuit is onboard or in communication with an NMR spectrometer foracquiring at least one NMR spectrum of an in vitro blood plasma or serumsample.
 31. The system of claim 29, wherein the at least one processoris configured to identify when a subject has an elevated concentrationof HP_(VL) relative to a population norm, wherein the subject has anelevated concentration of HP_(VL) when the subject's HP_(VL)concentration is ≧80% of a population norm, and wherein the at least oneprocessor is configured to provide a CHD risk assessment using the atleast one HDL interaction risk parameter only when the subject has theelevated concentration of HP_(VL).
 32. The system of claim 29, whereinthe at least one processor is configured to screen subjects that maybenefit from an HDL risk stratification test by (a) first identifying ifthe subject has elevated high density lipoprotein-cholesterol (HDL-C)that is at least one of: ≧60 mg/dL, ≧80 mg/dL, or ≧100 mg/dL; then (b)identifying when the subject also has an elevated concentration ofHP_(VL) that is ≧80% of a population norm.
 33. The system of claim 29,wherein the HP_(VS) includes HDL subpopulations having an averagediameter between 7.4 nm and 7.6 nm, wherein the HP_(ML) includes HDLsubpopulations having an average diameter between 8.3 nm and 10.9 nm,and wherein the HP_(VL) includes HDL subpopulations having an averagediameter between 11 nm and 13.5 nm.
 34. The system of claim 29, whereinthe at least one processor is configured to deconvolve an NMR compositesignal into 26 subpopulations (H1-H26) of different sizes of HDL-Pranging from a smallest HDL-P size associated with H1 to a largest HDL-Psize associated with H26, then: calculate concentrations of H1 and H2 togenerate the HP_(VS); calculate concentrations of H9-H20 to generate theHP_(ML); and calculate concentrations of H21 and H26 to generate theHP_(VL).
 35. The system of claim 29, wherein the at least one HDLinteraction risk parameter includes P1/R1.
 36. The system of claim 35,wherein the subject is identified as at risk for CHD if P1/R1 has avalue in the first or second quartile of a population norm.
 37. Thesystem of claim 35, wherein the subject is identified as at risk for CHDif P1/R1 has a value ≦170 μmol²/L².
 38. The system of claim 29, whereinthe at least one processor is configured to generate a report thatvisually and/or textually indicates whether the subject is at increasedrisk of CHD despite having elevated HP_(VL).
 39. The system of claim 29,wherein the at least one processor is configured to monitor whetherthere is a change in the at least one HDL risk interaction parameterover time to assess a change in CHD risk when HP_(VL) remains above 1.84μmol/L.
 40. The system of claim 29, wherein the subject is mammalian,and wherein the at least one processor is configured to obtain NMRsignal data of an in vitro blood plasma or serum sample of the subjectto determine NMR derived concentration measurements, then provide areport indicating whether the subject is at risk of having and/ordeveloping CHD based, in part, on the calculated at least one HDLinteraction risk parameter.